{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "pyMRST.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "jQLR7ptQlujB"
      ],
      "toc_visible": true,
      "authorship_tag": "ABX9TyPpSq9NSN2XhviuN50layze",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yohanesnuwara/pyMRST/blob/main/notebooks/pyMRST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O3_fpJXZ8d0G",
        "outputId": "8e958cdb-4d7f-4b39-f024-673568aebadb"
      },
      "source": [
        "# Clone pyMRST\r\n",
        "!git clone https://github.com/yohanesnuwara/pyMRST"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'pyMRST'...\n",
            "remote: Enumerating objects: 69, done.\u001b[K\n",
            "remote: Counting objects: 100% (69/69), done.\u001b[K\n",
            "remote: Compressing objects: 100% (69/69), done.\u001b[K\n",
            "remote: Total 201 (delta 38), reused 0 (delta 0), pack-reused 132\u001b[K\n",
            "Receiving objects: 100% (201/201), 161.10 KiB | 1.25 MiB/s, done.\n",
            "Resolving deltas: 100% (84/84), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rqfpKzMp8zZk"
      },
      "source": [
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "# Add directory where you install PyMRST\r\n",
        "import sys\r\n",
        "sys.path.append(\"/content/pyMRST\") \r\n",
        "\r\n",
        "import pymrst\r\n",
        "from pymrst_units import *"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1OIu4smY9jqx"
      },
      "source": [
        "# Setup PyMRST (Takes about 3 minutes)\r\n",
        "pymrst.setup()"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQLR7ptQlujB"
      },
      "source": [
        "## Functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VWSxxwiUlp75"
      },
      "source": [
        "def write_input(nx, ny, nz, lx, ly, lz, poro, k, rock, fluid, well, \r\n",
        "                bc_front, bc_back, bc_left, bc_right, \r\n",
        "                numSteps=None, totTime=None, steps=None):\r\n",
        "  \"\"\"\r\n",
        "  Convert inputs given in Python to write a MATLAB program that executes\r\n",
        "  reservoir geometry, rock property, fluid, boundary condition creation \r\n",
        "  \"\"\"\r\n",
        "  input = \"addpath /content/pyMRST \\n\"\r\n",
        "  input += \"addpaths\\n\\n\"\r\n",
        "\r\n",
        "  # # Timestep\r\n",
        "  # input += \"# Timestep\\n\"\r\n",
        "  # input += \"[numSteps, totTime] = deal({}, {}*day); \\n\".format(numSteps, totTime)\r\n",
        "  # input += \"steps = {}; \\n\\n\".format(steps)\r\n",
        "\r\n",
        "  # Reservoir geometry\r\n",
        "  input += \"# Reservoir geometry\\n\"\r\n",
        "  input += \"[nx,ny,nz] = deal({}, {}, {});\\n\".format(nx, ny, nz)\r\n",
        "  input += \"[lx,ly,lz] = deal({}, {}, {});\\n\".format(lx, ly, lz)\r\n",
        "  input += \"G = cartGrid([nx ny nz],[lx ly lz]);\\n\"\r\n",
        "  input += \"G = computeGeometry(G);\\n\\n\"\r\n",
        "\r\n",
        "  # Porosity \r\n",
        "  input += \"# Porosity \\n\"\r\n",
        "  if poro[\"type\"]==\"heterogeneous\":\r\n",
        "    if poro[\"field\"]==\"gaussian\":\r\n",
        "      input += \"p = gaussianField(G.cartDims, [{} {}], [5 3 1], {});\\n\\n\".format(poro[\"min\"], poro[\"max\"], poro[\"std\"])\r\n",
        "\r\n",
        "  # Permeability \r\n",
        "  input += \"# Permeability \\n\"\r\n",
        "  if k[\"type\"]==\"heterogeneous\":\r\n",
        "    if k[\"field\"]==\"kozeny\":\r\n",
        "      input += \"K = p.^3.*(1e-5)^2./(0.81*72*(1-p).^2); \\n\\n\"\r\n",
        "\r\n",
        "  # Make rock\r\n",
        "  input += \"# Make rock\\n\"\r\n",
        "  input +=  \"rock = makeRock(G, K(:), p(:)); \\n\\n\"\r\n",
        "\r\n",
        "  # Rock PV calculation\r\n",
        "  input += \"# Rock PV \\n\"\r\n",
        "  if fluid[\"type\"]==\"oil\": # Slightly compressible simulation\r\n",
        "    input += \"cr = {}; \\n\".format(rock[\"c\"])\r\n",
        "    input += \"p_r = {}; \\n\".format(rock[\"p_r\"])\r\n",
        "    input += \"pv_r = poreVolume(G, rock); \\n\"\r\n",
        "    input += \"pv = @(p) pv_r .* exp( cr * (p - p_r) ); \\n\\n\" \r\n",
        "\r\n",
        "  # Boundary conditions\r\n",
        "  input += \"# Boundary conditions \\n\"\r\n",
        "  input += \"bc = [];\\n\"\r\n",
        "\r\n",
        "  bc_loc = [\"'FRONT'\", \"'BACK'\", \"'LEFT'\", \"'RIGHT'\"]\r\n",
        "  bc_type = [bc_front[\"type\"], bc_back[\"type\"], bc_left[\"type\"], bc_right[\"type\"]]\r\n",
        "  bc_val = [bc_front[\"value\"], bc_back[\"value\"], bc_left[\"value\"], bc_right[\"value\"]]\r\n",
        "\r\n",
        "  for i in range(len(bc_type)):\r\n",
        "    if i==0:\r\n",
        "      # First boundary condition, bc=[]\r\n",
        "      if bc_type[i]==\"fluxside\":\r\n",
        "        input += \"bc = fluxside([], G, {}, {});\\n\".format(bc_loc[i], bc_val[i])\r\n",
        "      if bc_type[i]==\"pside\":\r\n",
        "        input += \"bc = pside([], G, {}, {});\\n\".format(bc_loc[i], bc_val[i])\r\n",
        "    if i>0:\r\n",
        "      if bc_type[i]==\"fluxside\":\r\n",
        "        input += \"bc = fluxside(bc, G, {}, {});\\n\".format(bc_loc[i], bc_val[i])\r\n",
        "      if bc_type[i]==\"pside\":\r\n",
        "        input += \"bc = pside(bc, G, {}, {});\\n\".format(bc_loc[i], bc_val[i])      \r\n",
        "  input += \"\\n\"\r\n",
        "\r\n",
        "  # Fluid\r\n",
        "  # If a string, so single-phase (numphase=1)\r\n",
        "  if type(fluid[\"type\"])==str:\r\n",
        "    input += \"# Fluid is {}\\n\".format(fluid[\"type\"])\r\n",
        "    # Single phase\r\n",
        "    if fluid[\"type\"]==\"water\":\r\n",
        "      input += \"fluid     = initSingleFluid('mu', {}, 'rho', {});\\n\\n\".format(fluid[\"mu\"], fluid[\"rho\"])\r\n",
        "    if fluid[\"type\"]==\"oil\":\r\n",
        "      input += \"mu = {}; \\n\".format(fluid[\"mu\"])\r\n",
        "      input += \"c = {}; \\n\".format(fluid[\"c\"])\r\n",
        "      input += \"rho_r = {}; \\n\".format(fluid[\"rho_r\"])\r\n",
        "      input += \"rhoS = {}; \\n\".format(fluid[\"rhoS\"])\r\n",
        "      input += \"rho = @(p) rho_r .* exp( c * (p - p_r) );\\n\\n\"\r\n",
        "      ## Timestep\r\n",
        "      input += \"# Timestep\\n\"\r\n",
        "      input += \"[numSteps, totTime] = deal({}, {}*day); \\n\".format(numSteps, totTime)\r\n",
        "      input += \"steps = {}; \\n\\n\".format(steps)      \r\n",
        "\r\n",
        "    if fluid[\"type\"]==\"gas\":\r\n",
        "      input += \"mu = @(p) {}*(1+{}*(p-{})); \\n\".format(fluid[\"mu0\"], fluid[\"c_mu\"], fluid[\"p_r\"])\r\n",
        "      input += \"@(p) {} .* exp( {} * (p - {}) );\\n\\n\".format(fluid[\"rho_r\"], fluid[\"c\"], fluid[\"p_r\"])\r\n",
        "      ## Timestep\r\n",
        "      input += \"# Timestep\\n\"\r\n",
        "      input += \"[numSteps, totTime] = deal({}, {}*day); \\n\".format(numSteps, totTime)\r\n",
        "      input += \"steps = {}; \\n\\n\".format(steps)  \r\n",
        "\r\n",
        "  else:\r\n",
        "    if len(fluid[\"type\"])==2:\r\n",
        "      # Two-phase\r\n",
        "      print(\"no\")\r\n",
        "\r\n",
        "  # Well\r\n",
        "  input += \"# Well\\n\"\r\n",
        "  if type(fluid[\"type\"])==str:\r\n",
        "    # Single-phase. Well doesn't have phase\r\n",
        "    for i in range(len(well[\"type\"])):\r\n",
        "      well_loc = well[\"cell_loc\"][i]\r\n",
        "      well_type = well[\"type\"][i]\r\n",
        "      well_value = well[\"value\"][i]\r\n",
        "      well_radius = well[\"radius\"][i]\r\n",
        "      well_skin = well[\"skin\"][i]\r\n",
        "      well_direction = well[\"direction\"][i]\r\n",
        "\r\n",
        "      # Well locations convert to list to avoid breaking into new line\r\n",
        "      input += \"well_loc = {};\".format(list(well_loc))  \r\n",
        "      input += \"\\n\"\r\n",
        "\r\n",
        "      if well_type==\"bhp\":\r\n",
        "        input += \"pwf = {}; \\n\".format(well_value)      \r\n",
        "\r\n",
        "      if i==0:\r\n",
        "        # First well\r\n",
        "        well_number = \"[]\"\r\n",
        "      if i>0:\r\n",
        "        # The next wells\r\n",
        "        well_number = \"W\"\r\n",
        "\r\n",
        "      if well_direction==\"y\":\r\n",
        "        # Well is horizontal to y\r\n",
        "        input += \"W = addWell({}, G, rock, well_loc, 'Type', '{}', 'InnerProduct', 'ip_tpf', 'Val', {}, 'Radius', {}, 'Dir', 'y');\\n\\n\".format(well_number, well_type, well_value, well_radius)\r\n",
        "      elif well_direction==\"x\":\r\n",
        "        # Well is horizontal to x\r\n",
        "        input += \"W = addWell({}, G, rock, well_loc, 'Type', '{}', 'InnerProduct', 'ip_tpf', 'Val', {}, 'Radius', {}, 'Dir', 'x');\\n\\n\".format(well_number, well_type, well_value, well_radius)      \r\n",
        "      else:\r\n",
        "        # Well is vertical\r\n",
        "        input += \"W = addWell({}, G, rock, well_loc, 'Type', '{}', 'InnerProduct', 'ip_tpf', 'Val', {}, 'Radius', {});\\n\\n\".format(well_number, well_type, well_value, well_radius)\r\n",
        "\r\n",
        "  else:\r\n",
        "    if len(fluid[\"type\"])==2:\r\n",
        "      # Two-phase. Well have phase\r\n",
        "      a=2  \r\n",
        "\r\n",
        "  # print(input)\r\n",
        "\r\n",
        "  # write file instead of %%writefile\r\n",
        "  # input_file = open(\"/content/INPUT.m\", \"w\")\r\n",
        "  input_file = open(\"/content/pyMRST/INPUT.m\", \"w\")\r\n",
        "  input_file.write(input)\r\n",
        "  input_file.close()  "
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V3tk1CLMlqsA"
      },
      "source": [
        "## Incompressible water"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zELV33xSmBBp"
      },
      "source": [
        "### Input"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xDfyklaGzah1"
      },
      "source": [
        "# Geometry\r\n",
        "nx, ny, nz = 30, 20, 10\r\n",
        "lx, ly, lz = 30, 20, 10\r\n",
        "\r\n",
        "# Reservoir property                                         \r\n",
        "poro = dict({\"type\": \"heterogeneous\", \r\n",
        "             \"field\": \"gaussian\", \r\n",
        "             \"min\": 0.2, \"max\": 0.4, \"std\": 2.5})\r\n",
        "\r\n",
        "k = dict({\"type\": \"heterogeneous\", \r\n",
        "          \"field\": \"kozeny\"})\r\n",
        "\r\n",
        "ntg = dict({\"type\": \"heterogeneous\", \r\n",
        "            \"field\": \"lognorm\", \r\n",
        "            \"min\": 0.4, \"max\": 0.6})\r\n",
        "\r\n",
        "rock = dict({\"c\": 1e-6/barsa(), # Rock compressibility at ref pressure, 1/bar to 1/Pa\r\n",
        "             \"p_r\": 200*barsa()}) # Reference pressure, bar to Pa             \r\n",
        "\r\n",
        "# Fluid property: 1-phase water\r\n",
        "fluid = dict({\"type\": \"water\", \r\n",
        "              \"mu\": 1*centi()*poise(), # cp to Pa.s \r\n",
        "              \"rho\": 1014})\r\n",
        "\r\n",
        "# Boundary \r\n",
        "bc_front = dict({\"type\": \"fluxside\", \"value\": 50*stb()/day()}) # bbl/d to m3/s\r\n",
        "bc_back = dict({\"type\": \"fluxside\", \"value\": 0})\r\n",
        "bc_left = dict({\"type\": \"fluxside\", \"value\": 100/day()}) # m3/d to m3/s\r\n",
        "bc_right = dict({\"type\": \"pside\", \"value\": 100*barsa()}) # bar to Pa\r\n",
        "\r\n",
        "# Well\r\n",
        "cell_loc1 = np.arange(10+60, nx*ny*nz, nx*ny)\r\n",
        "cell_loc2 = np.arange(nx, nx*ny, ny)\r\n",
        "\r\n",
        "# well = dict({\"cell_loc\": [cell_loc1, cell_loc2], \r\n",
        "#              \"type\": [\"rate\", \"bhp\"], \r\n",
        "#              \"value\": [(-10*stb()/day()), (110*barsa())], # bbl/d to m3/s, bar to Pa \r\n",
        "#              \"radius\": [.1, .1],\r\n",
        "#              \"skin\": [0, 0], \r\n",
        "#              \"direction\": [None, \"y\"],\r\n",
        "#              \"phase\": [[1,0], [0,1]]}) # Phase  \r\n",
        "\r\n",
        "# well = dict({\"cell_loc\": [cell_loc1], \r\n",
        "#              \"type\": [\"rate\"], \r\n",
        "#              \"value\": [(-10*stb()/day())], # bbl/d to m3/s, bar to Pa \r\n",
        "#              \"radius\": [.1],\r\n",
        "#              \"skin\": [0], \r\n",
        "#              \"direction\": [None]})  \r\n",
        "\r\n",
        "well = dict({\"cell_loc\": [cell_loc1], \r\n",
        "             \"type\": [\"bhp\"], \r\n",
        "             \"value\": [100*1e5], # bbl/d to m3/s, bar to Pa \r\n",
        "             \"radius\": [.1],\r\n",
        "             \"skin\": [0], \r\n",
        "             \"direction\": [None]})  \r\n",
        "\r\n",
        "# Execute function\r\n",
        "write_input(nx, ny, nz, lx, ly, lz, poro, k, rock, fluid, well, \r\n",
        "            bc_front, bc_back, bc_left, bc_right)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sHCR7CiJpOjL"
      },
      "source": [
        "### Execute "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7oTcYO1zJs1V"
      },
      "source": [
        "def water_1phase(nx, ny, nz, lx, ly, lz, poro, k, fluid, well, \r\n",
        "                 bc_front, bc_back, bc_left, bc_right):\r\n",
        "  \"\"\"\r\n",
        "  MRST Incompressible Water Simulation\r\n",
        "  \"\"\"\r\n",
        "  import oct2py as op\r\n",
        "  # Execute writing MATLAB input program from given inputs\r\n",
        "  # After executed, new .m file is created: INPUT.m\r\n",
        "  write_input(nx, ny, nz, lx, ly, lz, poro, k, rock, fluid, well, \r\n",
        "              bc_front, bc_back, bc_left, bc_right)\r\n",
        "  \r\n",
        "  # Execute simulation program \"water_1phase.m\"\r\n",
        "  # After executed, new .mat files (that contains PRESSURE, PORO, PERM result)\r\n",
        "  # is created inside new directory \"result_water_1phase\"\r\n",
        "  # !octave -W /content/pyMRST/water_1phase.m\r\n",
        "  octave = op.Oct2Py()\r\n",
        "  octave.run(\"/content/pyMRST/water_1phase.m\")"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ryzJRhlhV-G4",
        "outputId": "31df45f6-bcce-4b42-fae8-d8071e880609"
      },
      "source": [
        "# Execute simulator\r\n",
        "# Simulator results are added in new directory \"result_water_1phase\"\r\n",
        "water_1phase(nx, ny, nz, lx, ly, lz, poro, k, fluid, well, \r\n",
        "             bc_front, bc_back, bc_left, bc_right)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "warning: function /content/mrst-core/utils/mex/md5sum.m shadows a core library function\n",
            "Computing one-sided transmissibilities...\tElapsed time is 0.00767589 seconds.\n",
            "warning: Inconsistent Number of Phases.  Using 1 Phase (=min([3, 1, 1])).\n",
            "ans = 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "guHtPhIMptQl"
      },
      "source": [
        "### Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "EhpnSQBU1XC0",
        "outputId": "bdd61459-1ed4-4cfa-9cba-46cd00858969"
      },
      "source": [
        "# Plot pressure, porosity, and permeability\r\n",
        "plt.figure(figsize=(12,8))\r\n",
        "\r\n",
        "# Inputs for plotting\r\n",
        "directory = \"/content/result_water_1phase\"\r\n",
        "filename = [\"pressure.mat\", \"poro.mat\", \"perm.mat\"]\r\n",
        "plane, position = \"xy\", 0\r\n",
        "\r\n",
        "# Plot\r\n",
        "titles = [\"Pressure\", \"Porosity\", \"Permeability\"]\r\n",
        "\r\n",
        "for i in range(3):\r\n",
        "  plt.subplot(2,2,i+1)\r\n",
        "  plt.title(\"{} on {} Plane at Slice {}\".format(titles[i], plane, position),\r\n",
        "          size=15, pad=12) \r\n",
        "\r\n",
        "  # Get cell data\r\n",
        "  cube = pymrst.getCellData(directory, filename[i], dimension=(nx,ny,nz))\r\n",
        "\r\n",
        "  # Convert units of pressure and permeability\r\n",
        "  if i==0:\r\n",
        "    # Convert metric (Pa) to field (psia)\r\n",
        "    cube = cube/barsa()  \r\n",
        "  if i==2:\r\n",
        "    # Convert metric (Pa.s) to field (md)\r\n",
        "    cube = cube/(milli()*darcy()) \r\n",
        "\r\n",
        "  # Plot cell data\r\n",
        "  pymrst.plotCellData(cube, plane, position, cmap=\"plasma\")\r\n",
        "\r\n",
        "plt.tight_layout()\r\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vq01CeM8oxzf"
      },
      "source": [
        "## Compressible oil simulation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5YveoAQnq7fJ"
      },
      "source": [
        "### Input"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ElR0G3Olo0zc"
      },
      "source": [
        "# Geometry\r\n",
        "nx, ny, nz = 30, 20, 10\r\n",
        "lx, ly, lz = 200, 200, 50\r\n",
        "\r\n",
        "# Reservoir property                                         \r\n",
        "poro = dict({\"type\": \"heterogeneous\", \r\n",
        "             \"field\": \"gaussian\", \r\n",
        "             \"min\": 0.2, \"max\": 0.4, \"std\": 2.5})\r\n",
        "\r\n",
        "k = dict({\"type\": \"heterogeneous\", \r\n",
        "          \"field\": \"kozeny\"})\r\n",
        "\r\n",
        "ntg = dict({\"type\": \"heterogeneous\", \r\n",
        "            \"field\": \"lognorm\", \r\n",
        "            \"min\": 0.4, \"max\": 0.6})\r\n",
        "\r\n",
        "# Rock property\r\n",
        "rock = dict({\"c\": 1e-6*(1/1e+5), # Rock compressibility at ref pressure, 1/bar to 1/Pa\r\n",
        "             \"p_r\": 200*1e+5}) # Reference pressure, bar to Pa\r\n",
        "\r\n",
        "# Fluid property: 1-phase oil\r\n",
        "fluid = dict({\"type\": \"oil\", \r\n",
        "              \"mu\": 5 * 0.001, # cp to Pa.s \r\n",
        "              \"rho_r\": 850, # Density at reference pressure\r\n",
        "              \"rhoS\": 750, # Density on the surface\r\n",
        "              \"p_r\": 200 * 1E+5, # Reference pressure, bar to Pa\r\n",
        "              \"c\": 1e-3*(1/1E+5)}) # Fluid compressibility, 1/bar to 1/Pa \r\n",
        "\r\n",
        "# Boundary \r\n",
        "bc_front = dict({\"type\": \"fluxside\", \"value\": 50 * 1.84e-6}) # bbl/d to m3/s\r\n",
        "bc_back = dict({\"type\": \"fluxside\", \"value\": 0})\r\n",
        "bc_left = dict({\"type\": \"fluxside\", \"value\": 100 * 1.1574e-5}) # m3/d to m3/s\r\n",
        "bc_right = dict({\"type\": \"pside\", \"value\": 100 * 1e+5}) # bar to Pa\r\n",
        "\r\n",
        "# Well\r\n",
        "# cell_loc1 = np.concatenate((np.arange(311,381+10,10), np.arange(411,481+10,10),\r\n",
        "#                             np.arange(412,482+10,10), np.arange(312,382+10,10), \r\n",
        "#                             np.array([491,391])))\r\n",
        "\r\n",
        "# well = dict({\"cell_loc\": [cell_loc1],\r\n",
        "#              \"type\": [\"bhp\"],\r\n",
        "#              \"value\": [100*1e5],\r\n",
        "#              \"radius\": [.1],\r\n",
        "#              \"skin\": [0],\r\n",
        "#              \"direction\": [\"y\"]})\r\n",
        "\r\n",
        "cell_loc1 = np.arange(10+60, nx*ny*nz, nx*ny)\r\n",
        "\r\n",
        "well = dict({\"cell_loc\": [cell_loc1], \r\n",
        "             \"type\": [\"bhp\"], \r\n",
        "             \"value\": [100*1e5], # bar to Pa \r\n",
        "             \"radius\": [.1],\r\n",
        "             \"skin\": [0], \r\n",
        "             \"direction\": [None]}) \r\n",
        "\r\n",
        "# Time step\r\n",
        "numSteps, totTime = 52, 365\r\n",
        "steps = [2, 5, 10, 20]\r\n",
        "\r\n",
        "# Execute function\r\n",
        "write_input(nx, ny, nz, lx, ly, lz, poro, k, rock, fluid, well, \r\n",
        "            bc_front, bc_back, bc_left, bc_right, numSteps, totTime, steps)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G9aHQ2piwdTr"
      },
      "source": [
        "### Matlab script"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B9K_L2bsyDbD"
      },
      "source": [
        "### Execute"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LwKby0wWcFoO"
      },
      "source": [
        "def oil_1phase(nx, ny, nz, lx, ly, lz, poro, k, fluid, well, \r\n",
        "               bc_front, bc_back, bc_left, bc_right, \r\n",
        "               numSteps, totTime, steps):\r\n",
        "  \"\"\"\r\n",
        "  MRST Slightly Compressible Oil Simulation (Constant Viscosity over Pressure)\r\n",
        "  \"\"\"\r\n",
        "  import oct2py as op\r\n",
        "  # Execute writing MATLAB input program from given inputs\r\n",
        "  # After executed, new .m file is created: INPUT.m\r\n",
        "  write_input(nx, ny, nz, lx, ly, lz, poro, k, rock, fluid, well, \r\n",
        "              bc_front, bc_back, bc_left, bc_right, numSteps, totTime, steps)\r\n",
        "  \r\n",
        "  # Execute simulation program \"oil_1phase.m\"\r\n",
        "  # After executed, new .mat files (that contains PRESSURE, PORO, PERM result)\r\n",
        "  # is created inside new directory \"result_oil_1phase\"\r\n",
        "  # !octave -W /content/pyMRST/oil_1phase.m\r\n",
        "  octave = op.Oct2Py()\r\n",
        "  octave.run(\"/content/pyMRST/oil_1phase.m\")"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yA3QfY2ycoZ_",
        "outputId": "3ad29330-e0da-415c-ae08-07bab6bda218"
      },
      "source": [
        "# Execute simulator\r\n",
        "# Simulator results are added in new directory \"result_oil_1phase\"\r\n",
        "oil_1phase(nx, ny, nz, lx, ly, lz, poro, k, fluid, well, \r\n",
        "           bc_front, bc_back, bc_left, bc_right, numSteps, totTime, steps)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "warning: function /content/mrst-core/utils/mex/md5sum.m shadows a core library function\n",
            "\n",
            "Time step 1: Time 0.00 -> 7.02 days\n",
            "  Iteration   1:  Res = 1.0021e+07\n",
            "  Iteration   2:  Res = 2.9707e-02\n",
            "  Iteration   3:  Res = 2.1349e-05\n",
            "  Iteration   4:  Res = 9.1910e-12\n",
            "\n",
            "Time step 2: Time 7.02 -> 14.04 days\n",
            "  Iteration   1:  Res = 1.2139e-01\n",
            "  Iteration   2:  Res = 8.2228e-04\n",
            "  Iteration   3:  Res = 4.7691e-08\n",
            "\n",
            "Time step 3: Time 14.04 -> 21.06 days\n",
            "  Iteration   1:  Res = 7.8036e-02\n",
            "  Iteration   2:  Res = 3.4144e-04\n",
            "  Iteration   3:  Res = 6.8400e-09\n",
            "\n",
            "Time step 4: Time 21.06 -> 28.08 days\n",
            "  Iteration   1:  Res = 6.6593e-02\n",
            "  Iteration   2:  Res = 2.2535e-04\n",
            "  Iteration   3:  Res = 2.4982e-09\n",
            "\n",
            "Time step 5: Time 28.08 -> 35.10 days\n",
            "  Iteration   1:  Res = 6.0507e-02\n",
            "  Iteration   2:  Res = 1.8175e-04\n",
            "  Iteration   3:  Res = 1.5339e-09\n",
            "\n",
            "Time step 6: Time 35.10 -> 42.12 days\n",
            "  Iteration   1:  Res = 5.5813e-02\n",
            "  Iteration   2:  Res = 1.5467e-04\n",
            "  Iteration   3:  Res = 1.1137e-09\n",
            "\n",
            "Time step 7: Time 42.12 -> 49.13 days\n",
            "  Iteration   1:  Res = 5.1671e-02\n",
            "  Iteration   2:  Res = 1.3322e-04\n",
            "  Iteration   3:  Res = 8.3613e-10\n",
            "\n",
            "Time step 8: Time 49.13 -> 56.15 days\n",
            "  Iteration   1:  Res = 4.7880e-02\n",
            "  Iteration   2:  Res = 1.1501e-04\n",
            "  Iteration   3:  Res = 6.2938e-10\n",
            "\n",
            "Time step 9: Time 56.15 -> 63.17 days\n",
            "  Iteration   1:  Res = 4.4379e-02\n",
            "  Iteration   2:  Res = 9.9299e-05\n",
            "  Iteration   3:  Res = 4.7265e-10\n",
            "\n",
            "Time step 10: Time 63.17 -> 70.19 days\n",
            "  Iteration   1:  Res = 4.1137e-02\n",
            "  Iteration   2:  Res = 8.5712e-05\n",
            "  Iteration   3:  Res = 3.5413e-10\n",
            "\n",
            "Time step 11: Time 70.19 -> 77.21 days\n",
            "  Iteration   1:  Res = 3.8134e-02\n",
            "  Iteration   2:  Res = 7.3963e-05\n",
            "  Iteration   3:  Res = 2.6488e-10\n",
            "\n",
            "Time step 12: Time 77.21 -> 84.23 days\n",
            "  Iteration   1:  Res = 3.5352e-02\n",
            "  Iteration   2:  Res = 6.3808e-05\n",
            "  Iteration   3:  Res = 1.9789e-10\n",
            "\n",
            "Time step 13: Time 84.23 -> 91.25 days\n",
            "  Iteration   1:  Res = 3.2774e-02\n",
            "  Iteration   2:  Res = 5.5034e-05\n",
            "  Iteration   3:  Res = 1.4770e-10\n",
            "\n",
            "Time step 14: Time 91.25 -> 98.27 days\n",
            "  Iteration   1:  Res = 3.0384e-02\n",
            "  Iteration   2:  Res = 4.7457e-05\n",
            "  Iteration   3:  Res = 1.1016e-10\n",
            "\n",
            "Time step 15: Time 98.27 -> 105.29 days\n",
            "  Iteration   1:  Res = 2.8170e-02\n",
            "  Iteration   2:  Res = 4.0916e-05\n",
            "  Iteration   3:  Res = 8.2115e-11\n",
            "\n",
            "Time step 16: Time 105.29 -> 112.31 days\n",
            "  Iteration   1:  Res = 2.6118e-02\n",
            "  Iteration   2:  Res = 3.5271e-05\n",
            "  Iteration   3:  Res = 6.1173e-11\n",
            "\n",
            "Time step 17: Time 112.31 -> 119.33 days\n",
            "  Iteration   1:  Res = 2.4215e-02\n",
            "  Iteration   2:  Res = 3.0400e-05\n",
            "  Iteration   3:  Res = 4.5549e-11\n",
            "\n",
            "Time step 18: Time 119.33 -> 126.35 days\n",
            "  Iteration   1:  Res = 2.2452e-02\n",
            "  Iteration   2:  Res = 2.6198e-05\n",
            "  Iteration   3:  Res = 3.3901e-11\n",
            "\n",
            "Time step 19: Time 126.35 -> 133.37 days\n",
            "  Iteration   1:  Res = 2.0818e-02\n",
            "  Iteration   2:  Res = 2.2573e-05\n",
            "  Iteration   3:  Res = 2.5220e-11\n",
            "\n",
            "Time step 20: Time 133.37 -> 140.38 days\n",
            "  Iteration   1:  Res = 1.9302e-02\n",
            "  Iteration   2:  Res = 1.9448e-05\n",
            "  Iteration   3:  Res = 1.8755e-11\n",
            "\n",
            "Time step 21: Time 140.38 -> 147.40 days\n",
            "  Iteration   1:  Res = 1.7898e-02\n",
            "  Iteration   2:  Res = 1.6754e-05\n",
            "  Iteration   3:  Res = 1.3942e-11\n",
            "\n",
            "Time step 22: Time 147.40 -> 154.42 days\n",
            "  Iteration   1:  Res = 1.6596e-02\n",
            "  Iteration   2:  Res = 1.4431e-05\n",
            "  Iteration   3:  Res = 1.0361e-11\n",
            "\n",
            "Time step 23: Time 154.42 -> 161.44 days\n",
            "  Iteration   1:  Res = 1.5389e-02\n",
            "  Iteration   2:  Res = 1.2429e-05\n",
            "  Iteration   3:  Res = 7.6974e-12\n",
            "\n",
            "Time step 24: Time 161.44 -> 168.46 days\n",
            "  Iteration   1:  Res = 1.4270e-02\n",
            "  Iteration   2:  Res = 1.0704e-05\n",
            "  Iteration   3:  Res = 5.7166e-12\n",
            "\n",
            "Time step 25: Time 168.46 -> 175.48 days\n",
            "  Iteration   1:  Res = 1.3232e-02\n",
            "  Iteration   2:  Res = 9.2170e-06\n",
            "\n",
            "Time step 26: Time 175.48 -> 182.50 days\n",
            "  Iteration   1:  Res = 1.2270e-02\n",
            "  Iteration   2:  Res = 7.9363e-06\n",
            "\n",
            "Time step 27: Time 182.50 -> 189.52 days\n",
            "  Iteration   1:  Res = 1.1378e-02\n",
            "  Iteration   2:  Res = 6.8330e-06\n",
            "\n",
            "Time step 28: Time 189.52 -> 196.54 days\n",
            "  Iteration   1:  Res = 1.0551e-02\n",
            "  Iteration   2:  Res = 5.8827e-06\n",
            "\n",
            "Time step 29: Time 196.54 -> 203.56 days\n",
            "  Iteration   1:  Res = 9.7844e-03\n",
            "  Iteration   2:  Res = 5.0642e-06\n",
            "\n",
            "Time step 30: Time 203.56 -> 210.58 days\n",
            "  Iteration   1:  Res = 9.0734e-03\n",
            "  Iteration   2:  Res = 4.3593e-06\n",
            "\n",
            "Time step 31: Time 210.58 -> 217.60 days\n",
            "  Iteration   1:  Res = 8.4142e-03\n",
            "  Iteration   2:  Res = 3.7524e-06\n",
            "\n",
            "Time step 32: Time 217.60 -> 224.62 days\n",
            "  Iteration   1:  Res = 7.8029e-03\n",
            "  Iteration   2:  Res = 3.2298e-06\n",
            "\n",
            "Time step 33: Time 224.62 -> 231.63 days\n",
            "  Iteration   1:  Res = 7.2360e-03\n",
            "  Iteration   2:  Res = 2.7798e-06\n",
            "\n",
            "Time step 34: Time 231.63 -> 238.65 days\n",
            "  Iteration   1:  Res = 6.7104e-03\n",
            "  Iteration   2:  Res = 2.3924e-06\n",
            "\n",
            "Time step 35: Time 238.65 -> 245.67 days\n",
            "  Iteration   1:  Res = 6.2230e-03\n",
            "  Iteration   2:  Res = 2.0589e-06\n",
            "\n",
            "Time step 36: Time 245.67 -> 252.69 days\n",
            "  Iteration   1:  Res = 5.7711e-03\n",
            "  Iteration   2:  Res = 1.7719e-06\n",
            "\n",
            "Time step 37: Time 252.69 -> 259.71 days\n",
            "  Iteration   1:  Res = 5.3520e-03\n",
            "  Iteration   2:  Res = 1.5248e-06\n",
            "\n",
            "Time step 38: Time 259.71 -> 266.73 days\n",
            "  Iteration   1:  Res = 4.9633e-03\n",
            "  Iteration   2:  Res = 1.3121e-06\n",
            "\n",
            "Time step 39: Time 266.73 -> 273.75 days\n",
            "  Iteration   1:  Res = 4.6029e-03\n",
            "  Iteration   2:  Res = 1.1290e-06\n",
            "\n",
            "Time step 40: Time 273.75 -> 280.77 days\n",
            "  Iteration   1:  Res = 4.2687e-03\n",
            "  Iteration   2:  Res = 9.7149e-07\n",
            "\n",
            "Time step 41: Time 280.77 -> 287.79 days\n",
            "  Iteration   1:  Res = 3.9588e-03\n",
            "  Iteration   2:  Res = 8.3591e-07\n",
            "\n",
            "Time step 42: Time 287.79 -> 294.81 days\n",
            "  Iteration   1:  Res = 3.6714e-03\n",
            "  Iteration   2:  Res = 7.1923e-07\n",
            "\n",
            "Time step 43: Time 294.81 -> 301.83 days\n",
            "  Iteration   1:  Res = 3.4048e-03\n",
            "  Iteration   2:  Res = 6.1883e-07\n",
            "\n",
            "Time step 44: Time 301.83 -> 308.85 days\n",
            "  Iteration   1:  Res = 3.1577e-03\n",
            "  Iteration   2:  Res = 5.3243e-07\n",
            "\n",
            "Time step 45: Time 308.85 -> 315.87 days\n",
            "  Iteration   1:  Res = 2.9284e-03\n",
            "  Iteration   2:  Res = 4.5808e-07\n",
            "\n",
            "Time step 46: Time 315.87 -> 322.88 days\n",
            "  Iteration   1:  Res = 2.7159e-03\n",
            "  Iteration   2:  Res = 3.9411e-07\n",
            "\n",
            "Time step 47: Time 322.88 -> 329.90 days\n",
            "  Iteration   1:  Res = 2.5187e-03\n",
            "  Iteration   2:  Res = 3.3906e-07\n",
            "\n",
            "Time step 48: Time 329.90 -> 336.92 days\n",
            "  Iteration   1:  Res = 2.3359e-03\n",
            "  Iteration   2:  Res = 2.9170e-07\n",
            "\n",
            "Time step 49: Time 336.92 -> 343.94 days\n",
            "  Iteration   1:  Res = 2.1663e-03\n",
            "  Iteration   2:  Res = 2.5096e-07\n",
            "\n",
            "Time step 50: Time 343.94 -> 350.96 days\n",
            "  Iteration   1:  Res = 2.0091e-03\n",
            "  Iteration   2:  Res = 2.1590e-07\n",
            "\n",
            "Time step 51: Time 350.96 -> 357.98 days\n",
            "  Iteration   1:  Res = 1.8633e-03\n",
            "  Iteration   2:  Res = 1.8573e-07\n",
            "\n",
            "Time step 52: Time 357.98 -> 365.00 days\n",
            "  Iteration   1:  Res = 1.7280e-03\n",
            "  Iteration   2:  Res = 1.5978e-07\n",
            "ans = 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MufPTZFGyFLo"
      },
      "source": [
        "### Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "4_1aPl_EGXYV",
        "outputId": "9df9912f-377a-4d73-fbc5-45f246df27cc"
      },
      "source": [
        "# Specify dimension\r\n",
        "dimension = (nx,ny,nz)\r\n",
        "\r\n",
        "# Input for plots\r\n",
        "plane, position = \"xy\", 2\r\n",
        "\r\n",
        "numSteps, totTime = 52, 365 # day\r\n",
        "steps = [2, 5, 10, 20]\r\n",
        "\r\n",
        "# Plot pressure for every timestep\r\n",
        "plt.figure(figsize=(10,8))\r\n",
        "\r\n",
        "for i in range(len(steps)):\r\n",
        "  directory = \"/content/result_oil_1phase/\"\r\n",
        "  filename = \"pressure{}.mat\".format(steps[i])\r\n",
        "\r\n",
        "  # Get cell data \r\n",
        "  cube = pymrst.getCellData(directory, filename, dimension=(nx,ny,nz))\r\n",
        "\r\n",
        "  # Convert to barsa\r\n",
        "  cube = cube/barsa()\r\n",
        "\r\n",
        "  # Plot for every timestep\r\n",
        "  day = totTime / numSteps * steps[i]\r\n",
        "  plt.subplot(2,2,i+1)\r\n",
        "  pymrst.plotCellData(cube, plane, position, cmap=\"plasma\")\r\n",
        "  plt.title(\"Pressure on {} Plane at Slice {} (Day {:.1f})\".format(plane, position, day),\r\n",
        "            size=15, pad=15)  \r\n",
        "\r\n",
        "plt.tight_layout(1.7)\r\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "ZILdm_WLE-vq",
        "outputId": "e63da675-ba43-4422-c2d7-a283af6d10a7"
      },
      "source": [
        "from pymrst_units import *\r\n",
        "\r\n",
        "# Plot well solutions\r\n",
        "directory = \"/content/result_oil_1phase/\"\r\n",
        "\r\n",
        "time = pymrst.getWellSol(directory, \"time.mat\") / day() \r\n",
        "qo = pymrst.getWellSol(directory, \"qo.mat\") / (stb()/day())\r\n",
        "Pwf = pymrst.getWellSol(directory, \"Pwf.mat\") / barsa() # convert to barsa\r\n",
        "\r\n",
        "plt.figure(figsize=(10,4))\r\n",
        "\r\n",
        "plt.subplot(1,2,1)\r\n",
        "plt.plot(time, Pwf, \".-\", color=\"purple\")\r\n",
        "plt.grid()\r\n",
        "plt.title(\"Pwf\")\r\n",
        "plt.xlabel(\"Day\"); plt.ylabel(\"Pwf [bar]\")\r\n",
        "plt.xlim(0,365)\r\n",
        "\r\n",
        "plt.subplot(1,2,2)\r\n",
        "plt.step(time, qo, color=\"green\")\r\n",
        "plt.grid()\r\n",
        "plt.title(\"Oil rate\")\r\n",
        "plt.xlabel(\"Day\"); plt.ylabel(\"Qo [STB/D]\")\r\n",
        "plt.xlim(0,365)\r\n",
        "\r\n",
        "plt.show()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}